What Is Point Cloud Processing Software?
Point cloud processing software is software that analyzes and models point cloud data acquired by a 3D laser scanner, etc., and converts it into data that can be used in architectural CAD (computer aided design).
Point cloud data acquired by a 3D laser scanner or other means is data that combines the XYZ coordinates of points arranged in a grid of equally spaced points in 3D space with color information and other data. Since point cloud data is only “point” information, it must be converted into “surface” or “three-dimensional” information that can be handled by architectural CAD software. Point cloud processing software is used to perform this conversion.
Uses of Point Cloud Processing Software
The main fields of application for point cloud processing software are civil engineering and construction, and factory and building construction. Point cloud data is acquired by 3D laser scanners and drones.
Civil engineering and construction applications include soil volume calculations using the mesh method and triangulation (a set of triangulated survey data), cross-sectional mapping at arbitrary locations, and contour line creation. The factory/building field includes modeling of piping, planes, steel, equipment measurements, and simulation of loading and unloading.
Point cloud processing software can also be used for virtual reality (VR) and augmented reality (AR) applications.
Principle of Point Cloud Processing Software
Point cloud data is a combination of 3D points acquired by a 3D laser scanner, etc. and color information.
1. How Point Cloud Data Is Acquired
A 3D laser scanner irradiates a laser beam onto an object and acquires information by detecting the reflected laser beam. The acquired information is a grid of points (XYZ coordinates at equal intervals) in 3D space (Cartesian coordinates), with corresponding color information (RGB values), etc. Currently, instead of a 3D laser scanner, a 3D laser scanner is used to acquire point cloud data.
Nowadays, point cloud data is increasingly being acquired by drones instead of 3D laser scanners. Since this point cloud data is only “point” information, it cannot be handled by software such as architectural CAD as it is.
Point cloud processing software is used to convert this “point” information into “surface” or “three-dimensional” information.
2. Processing Method of Point Cloud Data
The flow of data processing in point cloud processing software is as follows:
- Acquisition of point cloud data
- Processing (alignment, noise reduction)
- Analysis (dimensional measurement, interference check)
- Modeling (creation of 3D model and mesh data)
- Final output (file creation in a format suitable for the application)
The output file is then loaded into CAD software or other software for use.
Other Information on Point Cloud Processing Software
1. ICP Algorithm
Most existing point cloud processing software uses an algorithm called ICP (iterative closest point) to calculate the alignment between different point cloud data by fitting.
It iteratively computes the correspondence between points and updates their relative position and orientation to shorten the sum of the distances between the corresponding points. In standard ICP, the process alternates between mapping using nearest neighbor points and geometric transformation.
Depending on the initial state of the positions and postures between the point clouds, the calculation results may be difficult to obtain accurate results.
2. Methods Implemented in Previous Studies
For positioning between scanned point clouds, there are methods that use planar information of road surfaces and buildings and methods that use color information. Methods that use this information in combination to perform stepwise alignment and are not affected by initial conditions or missing data have been proposed in recent years.
Scan data integration methods that use information obtained from SfM are also effective. For areas where there is little overlap between scan data, multiple photographs are taken and the 3D information is supplemented by SfM. This method improves the accuracy of scan data merging.